نوع مقاله : مقاله پژوهشی

نویسندگان

• شیوا سادات قاسمی ¹
• عباس خمسه ²
• سیدجواد ایرانبان ³

¹ دانشجوی دکتری، گروه مدیریت تکنولوژی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

² دانشیارگروه مدیریت صنعتی، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران نویسنده مسئول: abbas.khamseh@kiau.ac.ir

³ استادیارگروه مدیریت، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران

چکیده

در چشم­ انداز معاصر صنایع فناوری محور، ادغام هوش مصنوعی در رصد فناوری برای تقویت نوآوری و حفظ رقابت ضروری است. هدف این پژوهش، توسعه چارچوبی برای رصد فناوری مبتنی بر هوش مصنوعی با تمرکز بر شرکت­های فناوری محور است. این پژوهش با رویکرد کیفی به گردآوری داده­ها از طریق روش فراترکیب سندلوسکی و بارسو پرداخت. این روش با بررسی نظام­مند 28 مقاله مرتبط با هدف پژوهش از بین 253 مقاله اولیه انجام شد. مقاله­های نهایی براساس معیارهای ورود به مطالعه انتخاب شدند. روایی پژوهش برطبق معیارها، برگزاری جلسات با اعضای تیم پژوهش، استفاده از کارشناس و ممیزی کل فرایند برای اجماع نظری تأیید شد و پایایی نیز از طریق برنامه مهارت­های ارزیابی انتقادی مشخص شد. چارچوب رصد فناوری مبتنی بر هوش مصنوعی در شرکت­های فناوری محوردر پنج بعد ترسیم شده است: ابزارهای رصد فناوری، چرخه عمر فناوری، محیط شرکت، رویکرد شرکت در مواجهه با محیط، و ظرفیت جذب شرکت. یافته‌ها بر نقش محوری ابزارهای رصد فناوری مبتنی بر هوش مصنوعی تأکید می‌کنند، پویایی‌های ظریف چرخه عمر فناوری را روشن می‌کنند، جنبه‌های چندوجهی محیط شرکت را آشکار می‌سازند، رویکردهای استراتژیک در مواجهه با چشم‌انداز فناوری درحال تحول را ترسیم می‌کنند، و بر ضرورت ظرفیت جذب برای استفاده موثر از فناوری­های هوش مصنوعی تأکید می‌کنند. این پژوهش با ارائه بینش‌های عملی و راهنمایی استراتژیک، شرکت‌های فناوری محور را با پایه‌ای قوی برای پیمایش در تقاطع پیچیده هوش مصنوعی و رصد فناوری مجهز می‌کند، درنتیجه رشد پایدار، افزایش مزیت رقابتی و نوآوری پایدار را تسریع می‌کند.

کلیدواژه‌ها

• هوش مصنوعی
• رصد فناوری
• شرکت های فناوری محور
• تحول دیجیتال

موضوعات

• رویکردهای مدیریتی در حوزه کسب‌ وکار هوشمند

عنوان مقاله [English]

Analyzing the Factors Affecting the Technology Scouting Based on Artificial Intelligence in technology-Oriented Companies

نویسندگان [English]

• Shiva sadat Ghasemi ¹
• Abbas khamseh ²
• Seyed Javad Iranban ³

¹ Ph.D. Student, Department of Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

² Associate Professor, Department of Industrial Management, Karaj Branch, Islamic Azad University, Karaj, Iran Corresponding Author: abbas.khamseh@kiau.ac.ir

³ Assistant Professor, Department of Management, Shiraz Branch, Islamic Azad University, Shiraz

چکیده [English]

In the contemporary landscape of technology-driven industries, the integration of artificial intelligence into technology scouting is imperative for enhancing innovation and sustaining competitiveness. This research aims to forge a framework for technology scouting based on artificial intelligence, with a specific focus on technology-based companies. Employing a qualitative approach, data collection utilized the meta-synthesis method devised by Sandelowski and Barroso. This involved a systematic review of 28 articles relevant to the research goal out of a pool of 253 primary articles. The final selection of articles was based on predefined inclusion criteria. The research's validity was confirmed through adherence to criteria, team meetings, expert consultations, and an exhaustive audit for theoretical consensus, while reliability was ascertained through the Critical Evaluation Skills Programme. The framework spans five dimensions: technology scouting tools, technology life cycle, firm environment, firm's approach to the environment, and firm's absorptive capacity. The findings underscore the pivotal role of AI-based technology scouting tools, elucidate the nuanced dynamics of the technology life cycle, and reveal the multifaceted aspects of the enterprise environment. The research outlines strategic approaches for navigating the evolving technology landscape, underscoring the imperative of absorptive capacity for the effective utilization of artificial intelligence technologies. By delivering actionable insights and strategic counsel, this research serves to furnish technology-based companies with a robust underpinning for negotiating the intricate intersection of AI and technology surveillance. In doing so, it propels sustainable growth, fortifies competitive advantage, and fosters enduring innovation.

Introduction

In the dynamic world of technology-driven industry, the role of strategic technology management, particularly in the technology selection and acquisition phases, cannot be overemphasized if success is sought in innovation-driven companies. Focusing on technology-oriented companies that currently face a rapid industrial evolution, the present study highlights the indispensable role of technology scouting, equipped specifically with artificial intelligence (AI), in grappling with the imminent competitive environment. The study proposes a framework that anticipates a future where AI plays a central role in technology acquisition and that strives to enhance absorptive capacities by bridging the adaptation gap. Drawing upon AI, the propsoed framework not only ensures proper technology selection by firms but also drives them toward cutting-edge technological innovations. Serving as a guide for decision-makers, technology strategists, and specialists, the study is expected to contribute, both theoretically and practically, to the understanding and advancement of technology scouting in tech-driven companies. Moreover, it explores and identifies the needs of organizations navigating the intricate technology landscape to derive actionable insights that ensure sustainable innovation leadership.
What is the framework for technology scouting based on artificial intelligence in technology-oriented companies?
 

Literature Review

In today's rapidly evolving tech landscape, it is essential to cope with the changing business environment (Kujawa and Paetzold, 2019). Ahammad et al. (2021) linked strategic agility to search strategies. Wang and Quan (2021) studied the impact of technology selection uncertainty on firms’ absorptive capacity. Vuorio et al. (2018) explored the significance of competitive edge in tech-driven enterprises. Kerr and Phall (2018) developed a scouting process model. Nasullaev et al. (2020) reiterated the alignment of strategy and tech scouting. Xu et al. (2021) advocated patent analysis in scouting. Sikandar et al. (2021) reiterated patents' innovation measure. Tabrizi et al. (2019) observed a shift to tech-centric business models. Stute et al. (2021) noted the importance of AI in supply chain enhancement. Mariani et al. (2023) classified the motivations underlying AI adoption. Stahl et al. (2023) addressed AI ethics while D'Almeida et al. (2022) categorized AI applications. Wang et al. (2020) identified AI algorithms. Despite these efforts, scant research has been reported on tech transformation, especially AI. This study adopts the meta-synthesis method to explore the digital transformation complexities, focusing on AI's transformative potential and bridging the gaps to derive a roadmap for navigating tech-driven industries.
 

Methodology

Employing a qualitative approach and the meta-synthesis method, a seven-step process (including goal setting, review, selection, extraction, analysis, quality control, and model development) was meticulously followed to develop an AI-based technological scouting model for advanced tech firms. A systematic search yielded 253 articles, 28 of which met the inclusion criteria and were validated through team meetings, software analysis, and expert consultation. Reliability was ensured since 89% of the articles received excellent scores via the Critical Evaluation Skills Program, indicating high quality.
 

Results

The research adopted a classified analysis perspective, utilizing inductive analysis based on Sandelowski and Barroso (2007). This involves extracting primary codes related to AI-based technology observation in high-tech companies, identifying patterns through open coding, and classifying concepts into sub-categories and main categories via axial coding.
 
 
Table 1
Factors Affecting AI-Based Technology Scouting




Category


Subcategory


Concepts




Technology Scouting Tool


Open Source Intelligence (OSINT) Tools


Web scraping tools, social media monitoring, online forums, patent databases, news aggregators, competitive intelligence tools, and data analytics platforms.




Machine Learning and AI Tools


Natural Language Processing (NLP), predictive analytics, pattern recognition, chatbots, sentiment analysis, machine learning, and cognitive computing tools.




Collaboration and Communication Platforms


Online collaboration tools, project management platforms, virtual team collaboration, idea management, crowdsourcing, communication apps, and workflow automation.




Technology Life Cycle


Innovation and Invention


Idea generation, R&D, concept testing, prototyping, patenting, technology transfer, proof of concept, funding, collaborative research, and feasibility studies.




Technology Adoption and Diffusion


Technology readiness, market analysis, adoption theories, market penetration, standardization, compliance, user testing, and overcoming adoption barriers.




Technology Evolution and Obsolescence


Continuous improvement, iterative development, versioning, obsolescence management, legacy systems, discontinuation planning, sustainability, disruptive tech, and sunset planning.




Company Environment


Competitive Landscape Analysis


Competitor mapping, SWOT analysis, industry benchmarking, market share analysis, competitive intelligence, PESTLE analysis, collaboration strategies, positioning, and sustainable advantage.




Regulatory and Legal Environment


Intellectual property management, standards compliance, regulatory impact, patent landscape analysis, legal risk, data protection, ethics, antitrust, government policies, and international regulations.




Internal Organizational Environment


Culture, cross-functional collaboration, governance, change management, talent, agile structures, infrastructure, decision-making, metrics, and employee engagement.




The Company's Approach in Facing the Environment


Innovation Strategy Formulation


Roadmapping, open innovation, blue ocean strategy, core competency analysis, innovation ecosystems, portfolio management, ambidextrous approach, horizon scanning, lean methodologies, and design thinking.




Adaptive and Resilient Practices


Crisis management, scenario planning, risk management, agile project management, supply chain resilience, continuous learning, adaptive capabilities, technology portfolio flexibility, and fostering innovation culture.




Strategic Alliances and Partnerships


Collaborative innovation, joint ventures, technology ecosystems, university-industry collaborations, innovation networks, open source, licensing, technology transfer, competition, and strategic partnerships.




Absorption Capacity of the Company


Learning and Knowledge Management


Organizational learning, knowledge creation, sharing platforms, communities of practice, intellectual capital, training programs, technology scouting, learning culture, and tacit knowledge transfer.




Resource Allocation and Utilization


Technology budgeting, allocation models, ROI analysis, portfolio management, cross-functional sharing, resource efficiency, project prioritization, dynamic reallocation, innovation finance, and risk management.




Adoption of Emerging Technologies


Scanning trends, piloting new tech, foresight methodologies, early adoption, readiness assessments, and collaborative ecosystems for adoption, mitigating risks, cross-functional teams, integration, and continuous monitoring.




 

Discussion

To address the crucial gap in technology scouting in technology-oriented companies involved in the joint AI and technology scouting, the study develops a framework of five dimensions. Open-source smart tools and machine learning are explored as essential components of the "Technology Scouting Tool"dimension to contribute to the development of a cohesive strategy. The "Technology Life Cycle" dimension guides the firm through the innovation, adoption, and evolution stages. The "Company Environment" dimension adopts a multifaceted approach, considering competitive analysis, regulatory factors, and internal dynamics. The strategic components of the "Firm's Approach to the Environment" underline the contributions of innovation strategy, adaptability, and alliances while "Firm's Absorptive Capacity" offers practical insights by underscoring learning, resource allocation, and technology adoption.
 

Conclusion

The proposed framework provides a strategy tailored for tech-oriented firms incorporating AI into scouting and offers strategic insights across the five dimensions to tackle nuanced challenges in the technology landscape. Advocating advanced open-source tools and strategic approaches, it explores the technology life cycle, considers diverse aspects of firm environment, and launches an AI-driven future. Acknowledging limitations and emphasizing proper deployment of AI, the study lays the foundations for future studies to validate and expand the framework while ensuring responsive and sustainable application of AI-based surveillance technologies in corporate contexts.
 
Keywords: Artificial intelligence, Technology scouting, Technology-oriented companies, Digital transformation.
 
 

کلیدواژه‌ها [English]

• Artificial intelligence
• technology scouting
• technology-based companies
• digital transformation